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In order to accurately describe real systems with seasonal disturbances,which normally appear monthly or quarterly cycles, a novel discrete greyseasonal model, abbreviated as , is put forward by incorporating the seasonaldummy variables into the conventional model. Moreover, the mechanism andproperties of this proposed model are discussed in depth, revealing theinherent differences from the existing seasonal grey models. For validation andexplanation purposes, the proposed model is implemented to describe threeactual cases with monthly and quarterly seasonal fluctuations (quarterly windpower production, quarterly PM10, and monthly natural gas consumption), incomparison with five competing models involving grey prediction models ,conventional econometric technology , and artificial intelligences .Experimental results from the cases consistently demonstrated that the proposedmodel significantly outperforms the other benchmark models in terms of severalerror criteria. Moreover, further discussions about the influences of differentsequence lengths on the forecasting performance reveal that the proposed modelstill performs the best with strong robustness and high reliability inaddressing seasonal sequences. In general, the new model is validated to be apowerful and promising methodology for handling sequences with seasonalfluctuations.

A novel discrete grey seasonal model and its applications